Federated Learning (FL), as an emergent paradigm in privacy-preserving machine learning,
has garnered significant interest from scholars and engineers across both academic and …

Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative
training Machine Learning models. According to this novel framework, multiple participants …

While centralized servers pose a risk of being a single point of failure, decentralized
approaches like blockchain offer a compelling solution by implementing a consensus …

Federated learning (FL) is a promising framework for distributed machine learning that trains
models without sharing local data while protecting privacy. FL exploits the concept of …

Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative
training Machine Learning models. According to this novel framework, multiple participants …

Federated learning (FL) is a promising decentralized deep learning technology, which
allows users to update models cooperatively without sharing their data. FL is reshaping …

Federated learning (FL) is a promising framework for distributed machine learning that trains
models without sharing local data while protecting privacy. FL exploits the concept of …

Federated Learning (FL) is a distributed machine learning paradigm that trains models
across multiple devices without exchanging users' data, thereby providing stronger data …

Federated Learning (FL) has made an essential step towards enhancing the privacy of
traditional model training. However, gaps in the conventional FL framework make it …

Federated learning (FL) is a promising framework for distributed machine learning that trains
models without sharing local data while protecting privacy. FL exploits the concept of …